Intelligent house home safety protection system

Abstract

The invention discloses an intelligent house home safety protection system, and relates to the technical field of home safety protection. Comprising an environment data acquisition module, a data preprocessing module, an abnormal behavior detection module, a multi-sensor data fusion module, an abnormal event verification module and an alarm feedback and user interaction module, the environmental parameters include but are not limited to temperature, humidity, illumination, motion detection data and sound data. According to the invention, the accuracy of smart home safety protection is improved through multi-sensor data fusion and a deep learning technology, and false alarms are reduced. The system analyzes sensor data in real time and accurately identifies security threats and conventional activities. The self-adaptive verification mechanism adjusts a strategy according to user behaviors and combines a multi-level alarm mechanism to ensure that the user responds in time in an emergency, so that the safety protection effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of home safety protection, and in particular to a smart house home safety protection system. Background Art

[0002] Smart home security refers to comprehensive security and monitoring of residential environments through intelligent technologies and equipment. Combining advanced technologies such as the Internet of Things, artificial intelligence, and big data, it enables real-time monitoring of the interior and surrounding environment, automatic detection of abnormal events, and alarm feedback through the installation of intelligent security equipment (such as smart door locks, cameras, sensors, and alarm systems). Users can check their home's security status anytime, anywhere, and even remotely control it through devices such as mobile phones and tablets, ensuring the personal and financial safety of family members. Smart home security not only improves the convenience and efficiency of traditional security systems, but also enables timely response to potential safety hazards and reduces the occurrence of safety incidents through intelligent analysis and early warning.

[0003] The existing technology has the following deficiencies:

[0004] False alarms are a significant technical challenge in existing smart home security technologies. With the widespread adoption of video surveillance and motion detection devices in smart security systems, sensors can trigger frequent false alarms due to weather changes, device failures, or external environmental interference (such as wind blowing curtains or animals accidentally touching sensors). While modern systems often use algorithmic optimization to reduce false alarms, if the device fails to accurately identify the alarm or the user fails to promptly address it, this can lead to "alarm fatigue." As users neglect frequent alarms, they often fail to pay sufficient attention when a real security incident occurs, resulting in a delay in taking timely action. This can lead to security incidents such as burglary, posing a serious threat to home safety and property.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a smart home security protection system, which improves the accuracy of the smart home security protection system and reduces false alarms through multi-sensor data fusion and deep learning technology. The system uses deep neural networks (DNN) and convolutional neural networks (CNN) to conduct deep learning by analyzing sensor data such as motion, sound, temperature and humidity in real time, accurately identifying security threats and routine activities, and avoiding false alarms caused by environmental changes and equipment failures. The adaptive abnormal event verification mechanism dynamically adjusts the protection strategy according to the user's behavior pattern to ensure that the system responds accurately and reduces false alarms. The multi-level alarm mechanism gradually enhances the alarm according to the threat level to ensure that users receive a timely response in an emergency, improve the home security protection capability, and solve the problems in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a smart home security protection system, comprising an environmental data acquisition module, a data preprocessing module, an abnormal behavior detection module, a multi-sensor data fusion module, an abnormal event verification module, and an alarm feedback and user interaction module:

[0008] Environmental data collection module, real-time collection of environmental parameter data inside and outside the house, including but not limited to temperature, humidity, light, motion detection data and sound data;

[0009] The data preprocessing module preprocesses the environmental parameters and removes background interference through denoising and filtering to obtain accurate sensor readings;

[0010] The abnormal behavior detection module uses deep learning algorithms to analyze sensor data based on pre-processed data to identify whether there is abnormal behavior;

[0011] The multi-sensor data fusion module combines data from multiple sensors for fusion analysis to improve the accuracy of anomaly detection. Data fusion is based on the correlation and time sequence of multi-sensor information to avoid false triggering of a single sensor.

[0012] The abnormal event verification module, after detecting a potential abnormality, further verifies the abnormality using a preset scenario library and determines whether it is a false alarm by comparing the user's historical behavior pattern with environmental changes;

[0013] The alarm feedback and user interaction module triggers an alarm if the abnormal event is confirmed to be a real threat, and notifies the user in real time through a mobile phone application or smart device; if it is determined to be a false alarm, it is automatically ignored and the user is prompted to adjust the protection parameters through the interface to avoid alarm fatigue.

[0014] Preferably, data preprocessing includes performing time domain and frequency domain analysis on the collected environmental parameter data to remove interference caused by environmental factors, and automatically adjusting the filter coefficient through an adaptive filtering algorithm to optimize data processing accuracy.

[0015] Preferably, anomaly detection and identification includes analyzing video surveillance data based on a convolutional neural network to identify movement patterns inside and outside the house, and analyzing sound data through a multi-layer perceptron to determine whether there is abnormal activity.

[0016] Preferably, the specific steps of anomaly detection and identification are as follows:

[0017] Calculate the abnormal value at the current moment to determine whether an abnormal event has occurred. The calculation expression is as follows:

[0018] Where A(t) is the abnormal value at the current moment, w i is the weight of the i-th sensor data, x i (t) is the measurement value of the i-th sensor at time t, b is the bias term, and n is the number of sensors.

[0019] Preferably, the multi-dimensional data fusion is based on the weighted average method, which performs weighted processing on the signals of different sensor data, and the weight value is dynamically adjusted according to the reliability of the sensor data and the real-time changes of the current environment.

[0020] Preferably, the specific steps of multi-dimensional data fusion are as follows:

[0021] The fused data is calculated by integrating the various data collected by multiple sensors. The calculation expression is as follows:

[0022] Where D f (t) is the fused sensor data, D i (t) is the processed data of the i-th sensor at time t, n is the number of sensors, α i is the data weight of the i-th sensor;

[0023] The data weight calculation expression is as follows:

[0024] Where, T i is the historical data timestamp of the i-th sensor, σ i is the time scale associated with the ith sensor, t is the current time, T j is the historical data timestamp of the jth sensor, σ j is the time scale associated with the jth sensor.

[0025] Preferably, abnormal event verification introduces a decision tree algorithm to compare current environmental data with historical behavior data to determine whether it is a normal environmental fluctuation or a real security threat. The verification results are classified according to the set threshold to accurately screen out false alarms.

[0026] Preferably, abnormal event verification further classifies historical data and current detection data by introducing a support vector machine algorithm, learns based on positive and negative sample data in the training set through the maximum margin principle, and adjusts the sensitivity of the sensor according to the verification results to reduce the false alarm rate.

[0027] Preferably, the alarm feedback and user interaction further includes dynamically adjusting the alarm threshold according to the user's geographic location and time information;

[0028] Alarm feedback and user interaction include receiving real-time alarms through users' smart watches, mobile phones and other devices, and providing real-time conversation functions, allowing users to control the smart home system through voice and gestures, and manually confirm and close alarms.

[0029] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0030] The present invention significantly improves the accuracy of smart home security systems by adopting multi-sensor data fusion and deep learning technology, and reduces the problem of false alarms caused by changes in the external environment, equipment failures or common household activities. By collecting and analyzing different types of sensor data (such as motion sensors, sound sensors, temperature and humidity sensors, etc.) in real time, and using deep neural networks (DNN) and convolutional neural networks (CNN) to perform deep learning processing on the data, the system can accurately identify and judge potential security threats and normal household activities. The weighted fusion of multi-sensor data further optimizes the data processing process, so that even in complex home environments, the system can effectively identify real threats and avoid the occurrence of false alarms. This precise anomaly detection capability reduces alarm fatigue caused by false alarms, allowing users to have greater confidence in the effectiveness of the system.

[0031] The adaptive abnormal event verification mechanism of the present invention can dynamically adjust the system's response strategy based on the user's historical behavior data and real-time environmental changes. This means that the system can automatically learn and adapt to the living habits and environmental characteristics of each family member, so as to make accurate judgments when encountering new security events. For example, when the system detects that the doors and windows are opened, it will first check whether the behavior is consistent with the regular work and rest of the family members. If the user usually opens the window or door at a specific time, the system will automatically judge it as a normal activity to avoid false alarms; conversely, if the behavior does not conform to the normal work and rest time, the system will immediately identify it as an abnormality and trigger an alarm. This adaptive mechanism can not only improve the security system's adaptability to different home environments, but also adjust the protection strategy in real time according to the behavioral changes of family members, thereby improving the intelligence level of the system and reducing unnecessary false alarms.

[0032] The multi-level alarm mechanism set in the present invention can gradually upgrade the alarm according to different security threat levels and the user's response, minimizing alarm fatigue and ensuring that users can be reminded in time in real emergency situations. The primary alarm is notified through smart devices (such as mobile phones, smart watches, etc.) to remind users to conduct preliminary checks; if the user does not respond, the system will gradually increase the frequency and volume of the alarm, or issue a stronger warning sound through other smart devices (such as home audio, TV, etc.). Ultimately, if there is still no timely response, the system will conduct manual intervention through the security company. This progressive alarm mechanism can ensure that when a real security threat comes, the user can be quickly warned and take corresponding measures without disturbing the user's daily life, thereby effectively improving the family's safety protection capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0034] Figure 1 This is a module diagram of the smart house home security protection system of the present invention. DETAILED DESCRIPTION

[0035] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0036] The present invention provides Figure 1 The smart home security protection system shown in the figure includes an environmental data acquisition module, a data preprocessing module, an abnormal behavior detection module, a multi-sensor data fusion module, an abnormal event verification module, and an alarm feedback and user interaction module:

[0037] Environmental data collection module, real-time collection of environmental parameter data inside and outside the house, including but not limited to temperature, humidity, light, motion detection data and sound data;

[0038] The data preprocessing module preprocesses the environmental parameters and removes background interference through denoising and filtering to obtain accurate sensor readings;

[0039] Data preprocessing includes time domain and frequency domain analysis of the collected environmental parameter data to remove interference caused by environmental factors, and automatically adjusts the filter coefficient through adaptive filtering algorithm to optimize data processing accuracy.

[0040] The abnormal behavior detection module applies deep learning algorithms to analyze sensor data based on pre-processed data to identify abnormal behaviors, including but not limited to movement, sound, and window vibrations.

[0041] Anomaly detection and recognition involves analyzing video surveillance data using a convolutional neural network (CNN) to identify movement patterns inside and outside the house, and analyzing sound data using a multi-layer perceptron (MLP) to determine whether there is any abnormal activity.

[0042] The specific steps for anomaly detection and identification are as follows:

[0043] Calculate the abnormal value at the current moment to determine whether an abnormal event has occurred. The calculation expression is as follows:

[0044] Where A(t) is the abnormal value at the current moment, w i is the weight of the i-th sensor data, x i (t) is the measurement value of the i-th sensor at time t, b is the bias term, and n is the number of sensors.

[0045] This step achieves the impact of different sensor data on the final judgment by assigning different weights to different sensors in the detection through weighted averaging of different sensor data, thereby reducing false alarms caused by misleading single sensor data. For example, motion sensor data and sound sensor data can be given higher weights based on their historical accuracy and stability, while temperature and humidity sensors may be given lower weights due to environmental factors. In this way, the system can process different types of data more intelligently, reduce false alarms caused by external factors (such as weather changes or interference from home appliances), and ensure the accuracy and timeliness of alarms. By dynamically adjusting the weighting formula, the system can adapt to different environmental changes and further improve the accuracy and response speed of abnormal event identification.

[0046] The multi-sensor data fusion module combines data from multiple sensors for fusion analysis to improve the accuracy of anomaly detection. Data fusion is based on the correlation and time sequence of multi-sensor information to avoid false triggering of a single sensor.

[0047] Multi-dimensional data fusion is based on the weighted average method, which weights the signals of different sensor data. The weight value is dynamically adjusted according to the reliability of the sensor data and the real-time changes of the current environment.

[0048] The specific steps of multi-dimensional data fusion are as follows:

[0049] The fused data is calculated by integrating the various data collected by multiple sensors. The calculation expression is as follows:

[0050] Where D f (t) is the fused sensor data, D i (t) is the processed data of the i-th sensor at time t, n is the number of sensors, α i is the data weight of the i-th sensor;

[0051] The data weight calculation expression is as follows:

[0052] Where, T i is the historical data timestamp of the i-th sensor, σ i is the time scale associated with the ith sensor, t is the current time, T j is the historical data timestamp of the jth sensor, σ j is the time scale associated with the jth sensor.

[0053] Through this step, the weight of each sensor is dynamically calculated and the sensor data is weighted fused to improve the accuracy and sensitivity of data fusion and enhance the accuracy of anomaly detection. Specifically, the weight factor α i The calculation of takes into account the historical stability and real-time nature of each sensor data. Historical data timestamp T i It is used to quantify the "reliability" of sensors. That is, sensors that are relatively stable and have no faults for a long time will have a higher weight, while sensors that have problems or are unstable will automatically have their weight reduced. In addition, σ i This is used to control the time scale of each sensor. If sensor data changes rapidly, the system automatically adjusts its weight to give it higher sensitivity; conversely, it reduces its weight to avoid over-responding to short-term unstable fluctuations. By weighted fusion of all sensor data, the system can more accurately capture various abnormal events and effectively avoid false alarms caused by single sensor failures or errors.

[0054] This dynamic weighting approach allows the system to adapt to varying environmental factors and sensor operating conditions, further enhancing the intelligence of security protection, reducing false alarms due to environmental factors or technical issues, and improving responsiveness to real security threats. Based on multi-sensor data fusion, the system's fault tolerance and accuracy are significantly enhanced, ensuring the safety of family members and the protection of property.

[0055] The abnormal event verification module, after detecting a potential abnormality, further verifies the abnormality using a preset scenario library and determines whether it is a false alarm by comparing the user's historical behavior pattern with environmental changes;

[0056] Abnormal event verification introduces a decision tree algorithm to compare current environmental data with historical behavior data to determine whether it is a normal environmental fluctuation or a real security threat. The verification results are classified according to the set threshold to accurately filter out false alarms.

[0057] Abnormal event verification further classifies historical data and current detection data by introducing the support vector machine (SVM) algorithm. Through the maximum margin principle, learning is performed based on the positive and negative sample data in the training set, and the sensitivity of the sensor is adjusted according to the verification results to reduce the false alarm rate.

[0058] The alarm feedback and user interaction module triggers an alarm if the abnormal event is confirmed to be a real threat, and notifies the user in real time through a mobile app or smart device. If it is determined to be a false alarm, it is automatically ignored and the user is prompted to adjust the protection parameters through the interface to avoid alarm fatigue.

[0059] Alarm feedback and user interaction also include dynamically adjusting the alarm threshold based on the user's geographic location and time information. If the user is in a specific time period or a specific location, the alarm sensitivity will be increased to cope with different life scenarios.

[0060] Alarm feedback and user interaction include receiving real-time alarms through users' smart watches, mobile phones and other devices, and providing real-time conversation functions, allowing users to control the smart home system through voice and gestures, and manually confirm and close alarms.

[0061] Implementation method one: The core concept of this implementation method is to use modern deep learning technology combined with multi-sensor data fusion methods to identify and judge potential security threats in the home environment through comprehensive analysis of environmental information. In this implementation method, the smart home system deploys various types of sensors, including infrared motion sensors, sound sensors, temperature and humidity sensors, smart cameras, door and window sensors, etc., covering every key area of the house. Each sensor has a specific function. For example, infrared motion sensors are used to detect changes in movement in the room, sound sensors are used to detect abnormal sound fluctuations (such as glass breaking, calling sounds, etc.), and temperature and humidity sensors are used to monitor environmental changes inside the house. Smart cameras monitor the home environment through video streams to analyze whether there are suspicious people or abnormal activities.

[0062] Data from these sensors is transmitted in real time to the control center via IoT technology. The processing unit within the control center performs preliminary data preprocessing on the sensor data. The core steps of data preprocessing include denoising, filtering, and feature extraction. Taking video data as an example, the system uses a convolutional neural network (CNN) to process surveillance video in real time, extracting possible abnormal behavior patterns. For sound data, a deep neural network (DNN) is used for feature extraction to identify unusual sounds such as breaking glass and fighting, enabling more accurate assessment of safety hazards. The results of data preprocessing serve as the basis for subsequent anomaly detection.

[0063] During anomaly detection and identification, the system employs deep learning algorithms to analyze sensor data, using convolutional neural networks (CNNs) for image data recognition and recurrent neural networks (RNNs) for time series data analysis (such as temperature and humidity changes, and movement patterns). Using training data sets, the system can identify common activity patterns in the home environment, such as family members' movement paths and daily household chores, thus distinguishing them from potential security threats.

[0064] To further improve accuracy, this implementation utilizes multi-sensor data fusion technology. In traditional security systems, a single sensor is susceptible to interference from the external environment, leading to false alarms. However, by fusing data from multiple sensors, the system can more accurately identify anomalies. For example, when an infrared sensor detects motion and triggers an alarm, the system simultaneously analyzes the camera's video stream to confirm whether it is a family member passing by. Similarly, if an acoustic sensor detects the sound of breaking glass, but the video surveillance system doesn't detect unusual human activity, the system can automatically identify it as environmental interference or a false alarm, thus avoiding false alarms.

[0065] Multi-dimensional data fusion relies on a weighted averaging method. The system dynamically adjusts the weight of each sensor based on its reliability and environmental changes, further improving recognition accuracy. When the system identifies a potential threat, it immediately triggers an alarm and notifies the user via a mobile app or other smart device. This intelligent fusion of multi-sensor data effectively avoids false alarms, reduces alarm fatigue, and ensures timely response to real threats.

[0066] Furthermore, during the verification process of abnormal events, this embodiment further verifies the authenticity of abnormal behavior through comparative analysis of historical and current data. The system compares currently collected sensor data with the user's historical behavior data to determine whether it represents normal daily life behavior or a real security threat. For example, if the system detects that a door or window is open at a time inconsistent with the user's daily habits, the system will automatically issue an alarm and notify the user for further confirmation. This adaptive verification mechanism can further reduce false alarms and ensure the efficient operation of the home security protection system.

[0067] Implementation 2: The core goal of this implementation is to reduce false alarms through adaptive mechanisms and ensure the system can respond quickly to real threats. Traditional smart home security systems often use fixed rules and parameters to determine anomalies, but these rules may not adapt to the complex and changing home environment, which can easily lead to frequent false alarms. Therefore, this implementation adopts a dynamic and adaptive verification mechanism that learns from the user's historical behavior patterns and combines them with current environmental data to intelligently determine whether a security threat is real.

[0068] Specifically, this implementation first collects user behavioral data through smart devices (such as mobile phones, smart watches, smart door locks, etc.). This data includes, but is not limited to, the user's daily routines, activity paths, door and window opening and closing habits, and changes in the home environment. By building a user behavior model, the system can accurately grasp the daily activity patterns and behavior patterns of family members. Based on this historical data, the system can continuously learn and adjust to form a personalized behavior pattern database, which can be dynamically compared and analyzed with real-time sensor data.

[0069] When a sensor triggers an alarm, the system automatically compares it with the user's historical behavior data to determine whether it conforms to the user's normal behavior pattern. For example, if a sensor detects that a door or window is open at 9 pm, the system will first check whether the user usually opens the window or door during this time; if so, the system will not issue an alarm. If it detects unusual behavior, such as a door or window being opened at 2 am, and the user does not usually do this behavior, the system will determine it as an anomaly and trigger an alarm. This dynamic behavior comparison system not only improves the accuracy of anomaly identification, but also adapts to the user's lifestyle in real time, reducing false alarms caused by environmental changes or normal activities.

[0070] The system also uses environmental information for auxiliary verification. For example, if the system detects abnormal weather conditions (such as a snowstorm or thunderstorm) in the environmental data returned by the weather station, which may interfere with the normal operation of the sensor, the system will reduce the sensitivity of the sensor to reduce false alarms caused by environmental changes. Under normal circumstances, the system will provide real-time feedback of alarm information to the user through the mobile app. Users can view the safety status through real-time video. If it is confirmed to be a false alarm, the alarm can be directly turned off through the interface to avoid frequent alarm interference.

[0071] This implementation also enables more comprehensive anomaly verification through multi-device collaboration on a cloud platform. When a user sets up safety mode on multiple smart devices in their home, these devices work together to perform multi-level verification. For example, if a door or window sensor sounds an alarm, smart cameras and motion sensors will work synchronously to check for unidentified individuals. If these verification steps fail to confirm an anomaly, the system automatically marks the alarm as a false alarm and temporarily disables the relevant alarm module.

[0072] Implementation Method 3: The core of this implementation method is to dynamically adjust sensor sensitivity and establish a multi-level alarm mechanism to respond to different environments and home conditions. Traditional security systems typically set fixed sensor sensitivity and cannot respond quickly to changes in the home environment. This may lead to false alarms caused by environmental changes (such as weather, season, or external noise). To address this problem, this implementation method dynamically adjusts sensor sensitivity to adapt to different usage scenarios, while incorporating a multi-level alarm mechanism to improve response speed and accuracy.

[0073] First, the system dynamically adjusts the sensitivity of each sensor by continuously analyzing changes in the external environment, such as weather, seasonal variations, and noise levels. For example, during cold winter months, a sudden drop in external temperature could cause sensor malfunctions or false alarms. The system automatically detects this change and adjusts the sensor's sensitivity to a lower level. Similarly, during stormy or foggy weather, the system automatically adjusts the sensor to reduce interference caused by external weather factors. This adaptive sensitivity adjustment ensures high accuracy across a wide range of environmental conditions.

[0074] In addition, this implementation also incorporates a multi-level alert mechanism to ensure users receive appropriate safety feedback in different scenarios. When the system detects a potential threat, it first sends a preliminary alert notification via the mobile app and smartwatch, prompting the user to check the live video stream to confirm the alert information. If the user fails to respond promptly, the system enters a second-level alert, further increasing the alarm volume and issuing warnings through other smart devices (such as TVs and speakers). If the system detects an anomaly that is not promptly addressed, it enters a third-level alert mechanism, providing an emergency notification to the security company or alarm center and initiating pre-set emergency response procedures.

[0075] This multi-level alert mechanism minimizes missed alerts and false alarms, ensuring users receive timely notifications and can respond promptly when security incidents occur. Furthermore, dynamic sensitivity adjustment effectively reduces false alarms caused by changing environmental factors, ensuring the intelligent protection system operates smoothly in a variety of complex environments and enhancing the overall effectiveness of home security.

[0076] The present invention significantly improves the accuracy of smart home security systems by adopting multi-sensor data fusion and deep learning technology, and reduces the problem of false alarms caused by changes in the external environment, equipment failures or common household activities. By collecting and analyzing different types of sensor data (such as motion sensors, sound sensors, temperature and humidity sensors, etc.) in real time, and using deep neural networks (DNN) and convolutional neural networks (CNN) to perform deep learning processing on the data, the system can accurately identify and judge potential security threats and normal household activities. The weighted fusion of multi-sensor data further optimizes the data processing process, so that even in complex home environments, the system can effectively identify real threats and avoid the occurrence of false alarms. This precise anomaly detection capability reduces alarm fatigue caused by false alarms, allowing users to have greater confidence in the effectiveness of the system.

[0077] The adaptive abnormal event verification mechanism of the present invention can dynamically adjust the system's response strategy based on the user's historical behavior data and real-time environmental changes. This means that the system can automatically learn and adapt to the living habits and environmental characteristics of each family member, so as to make accurate judgments when encountering new security events. For example, when the system detects that the doors and windows are opened, it will first check whether the behavior is consistent with the regular work and rest of the family members. If the user usually opens the window or door at a specific time, the system will automatically judge it as a normal activity to avoid false alarms; conversely, if the behavior does not conform to the normal work and rest time, the system will immediately identify it as an abnormality and trigger an alarm. This adaptive mechanism can not only improve the security system's adaptability to different home environments, but also adjust the protection strategy in real time according to the behavioral changes of family members, thereby improving the intelligence level of the system and reducing unnecessary false alarms.

[0078] The multi-level alarm mechanism set in the present invention can gradually upgrade the alarm according to different security threat levels and the user's response, minimizing alarm fatigue and ensuring that users can be reminded in time in real emergency situations. The primary alarm is notified through smart devices (such as mobile phones, smart watches, etc.) to remind users to conduct preliminary checks; if the user does not respond, the system will gradually increase the frequency and volume of the alarm, or issue a stronger warning sound through other smart devices (such as home audio, TV, etc.). Ultimately, if there is still no timely response, the system will conduct manual intervention through the security company. This progressive alarm mechanism can ensure that when a real security threat comes, the user can be quickly warned and take corresponding measures without disturbing the user's daily life, thereby effectively improving the family's safety protection capabilities.

[0079] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0080] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0081] It should be noted that, in this document, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0082] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0083] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0084] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0085] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0086] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0087] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0088] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. Smart house home security protection system, characterized by: It includes environmental data acquisition module, data preprocessing module, abnormal behavior detection module, multi-sensor data fusion module, abnormal event verification module, and alarm feedback and user interaction module: Environmental data collection module, real-time collection of environmental parameter data inside and outside the house, including but not limited to temperature, humidity, light, motion detection data and sound data; The data preprocessing module preprocesses the environmental parameters and removes background interference through denoising and filtering to obtain accurate sensor readings; The abnormal behavior detection module uses deep learning algorithms to analyze sensor data based on pre-processed data to identify whether there is abnormal behavior; The multi-sensor data fusion module combines data from multiple sensors for fusion analysis to improve the accuracy of anomaly detection. Data fusion is based on the correlation and time sequence of multi-sensor information to avoid false triggering of a single sensor. The abnormal event verification module, after detecting a potential abnormality, further verifies the abnormality using a preset scenario library and determines whether it is a false alarm by comparing the user's historical behavior pattern with environmental changes; The alarm feedback and user interaction module triggers an alarm if the abnormal event is confirmed to be a real threat, and notifies the user in real time through a mobile phone application or smart device; if it is determined to be a false alarm, it is automatically ignored and the user is prompted to adjust the protection parameters through the interface to avoid alarm fatigue.

2. The smart house safety protection system according to claim 1, characterized in that: Data preprocessing includes time domain and frequency domain analysis of the collected environmental parameter data to remove interference caused by environmental factors, and automatically adjusts the filter coefficient through adaptive filtering algorithm to optimize data processing accuracy.

3. The smart house safety protection system according to claim 1, characterized in that: Anomaly detection and recognition involves analyzing video surveillance data using convolutional neural networks to identify movement patterns inside and outside the house, and analyzing sound data using a multi-layer perceptron to determine whether there is any abnormal activity.

4. The smart house safety protection system according to claim 1, characterized in that: The specific steps for anomaly detection and identification are as follows: Calculate the abnormal value at the current moment to determine whether an abnormal event has occurred. The calculation expression is as follows: Where A(t) is the abnormal value at the current moment, w i is the weight of the i-th sensor data, x i (t) is the measurement value of the i-th sensor at time t, b is the bias term, and n is the number of sensors.

5. The smart house home security protection system according to claim 1, characterized in that: Multi-dimensional data fusion is based on the weighted average method, which weights the signals of different sensor data. The weight value is dynamically adjusted according to the reliability of the sensor data and the real-time changes of the current environment.

6. The smart house safety protection system according to claim 1, characterized in that: The specific steps of multi-dimensional data fusion are as follows: The fused data is calculated by integrating the various data collected by multiple sensors. The calculation expression is as follows: Where D f (t) is the fused sensor data, D i (t) is the processed data of the i-th sensor at time t, n is the number of sensors, α i is the data weight of the i-th sensor; The data weight calculation expression is as follows: Where, T i is the historical data timestamp of the i-th sensor, σ i is the time scale associated with the ith sensor, t is the current time, T j is the historical data timestamp of the jth sensor, σ j is the time scale associated with the jth sensor.

7. The smart house safety protection system according to claim 1, characterized in that: Abnormal event verification introduces a decision tree algorithm to compare current environmental data with historical behavior data to determine whether it is a normal environmental fluctuation or a real security threat. The verification results are classified according to the set threshold to accurately filter out false alarms.

8. The smart house safety protection system according to claim 1, characterized in that: Abnormal event verification further classifies historical data and current detection data by introducing the support vector machine algorithm. Through the maximum margin principle, learning is performed based on the positive and negative sample data in the training set, and the sensitivity of the sensor is adjusted according to the verification results to reduce the false alarm rate.

9. The smart house safety protection system according to claim 1, characterized in that: Alarm feedback and user interaction also include dynamically adjusting alarm thresholds based on the user's geographic location and time information; Alarm feedback and user interaction include receiving real-time alarms through users' smart watches, mobile phones and other devices, and providing real-time conversation functions, allowing users to control the smart home system through voice and gestures, and manually confirm and close alarms.

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